What AI and predictive analytics can do for your business: a quick guide

AI-based predictive analytics: the basics

Artificial intelligence (AI)-based predictive analytics is when historical data is used to make accurate predictions, validate patterns and test assumptions.

Let’s unpack that… ?

Predictive analytics takes data collected from customers, inventory, sales and marketing activities. Then, the data is combined to forecast outcomes accurately, suggest improvements in operations and reduce costs by creating better workflows and systems. 

AI predictive analytics tools are so advanced that they can take historical buying behaviors and merge them with seasonal patterns and products to predict output and revenue. It’s used across various industries, from retail and consumer to manufacturing and IT, to help business leaders make complex decisions and predictions faster and more accurately. 

For marketing and sales teams, predictive analytics helps to analyze and predict customer buying behavior, giving decision makers a more accurate way to create forecasts and make sense of datasets packed with information like customer demographics, buying patterns and acquisition costs.

In the past, these datasets may have sat in a chunky spreadsheet gathering dust, but companies can now use them to build better, effective campaigns and create more accurate messaging to target customers when they’re ready to buy and boost sales. 

Although data is the key to unlocking customer behavior patterns and giving them a better experience, it’s essential to know how each data tool is used. On the surface, AI and predictive analytics may look similar, but there are some key differences to be aware of ?

AI vs. predictive analytics: what’s the difference?

Don’t be fooled – although AI data tools and predictive analytics sound like they do the same thing, the main differences lie in how decisions are made. 

AI is just that: artificial. Data is collected automatically, and algorithms make decisions and predictions artificially without involving humans. Using machine learning, AI also uses historical datasets and buyer behavior to make assumptions and reassess predictions solely on its own. Businesses trust that AI is making the best decisions for them with the available data as it’s done without the help of employees.

Predictive analytics is a little different. Datasets are still gathered and used in decision making, but humans ultimately test assumptions and accurately identify trends and patterns within them. 

Here’s the difference between the two tools in a real-life scenario. 

CRM giant HubSpot uses AI and predictive analytics to grow its customer base. Thanks to its machine learning and natural language processing technology, HubSpot automatically triggers messages to send to prospects based on certain events (like a job change or product launch) to increase their chances of getting a response.

An example of an outreach email sent from DeepGraph, an AI tool acquired by HubSpot to help with sales rep outreach.
Image source: TechCrunch

Predictive analytics is then used to “score” leads and alert sales reps to prospects likely to respond to their pitch.

Image source: HubSpot

Not only does predictive analytics help sales reps close more deals, but the data creates more accurate target buyer personas based on historical data, demographics and buying behavior patterns.

Three AI predictive analytics examples

1. Hostelworld produces personalized experiences with predictive analytics

Hostels may be marketed as budget accommodation, but Hostelworld still wanted to give its audience a personalized experience when they were traveling. 

To help travelers connect and find the right hostel, Hostelworld knew its app needed to deliver a customized journey that recommended accommodation and activities based on their behavior.

Image source: Hostelworld

Using predictive analytics, Hostelworld’s marketing team gathered in-depth data to see where customers are coming from, where they’re going and what they want to do in a destination once they get there. The data is then used to build more personalized campaigns and audience segments for more targeted advertising.

If a traveler uses the app to book a hostel in New York, they’ll see customized banners recommending activities in the area. Once they arrive in New York, the app sends push notifications about trendy restaurants near them, popular theaters in the area and the best places to shop –helping them have an unforgettable stay in The Big Apple.

The results of leaning on predictive analytics for its marketing efforts? 61% of bookings through Hostelworld’s platforms now come from repeat customers.

2. Walmart uses predictive analytics to restock fruit

Walmart collects so much data on its customers that it built its own analytics hub to collect up to 2.5 petabytes every hour.

Walmart’s analytics hub is optimizing marketing, pricing and customer patterns thanks to the help of analytics. Image source: Harvard Business School

Datasets around the economy, telecoms, social media, meteorology and local events are pulled into databases. Predictive analytics and Walmart’s AI algorithms then get to work on optimizing every single inch of the company’s organization. 

Recently, Walmart began digitizing brick-and-mortar stores using cameras and sensors to optimize inventories. If a banana didn’t look fresh by its color or the shelves looked empty, alerts are sent to the store telling them to restock or freshen up the section. Once the sensors and cameras collect enough data like availability and buying behaviors, Walmart can use it to build accurate forecasts for that store and improve its products. 

The real message in what Walmart is doing is that historical data is no longer enough. Companies must continuously collect data in real time and learn from changing buyer behaviors to be able to quickly pivot and always meet their demands.  

McKinsey agrees, saying that what succeeded in the past is now a poor predictor of the future, and analytics is helping to inform and unlock new pockets of growth.

3. Speedy uses bespoke forecasting algorithms to stabilize supply chains

Like many rental services, Speedy needs to have adequate equipment available across its depots to support its customers across industries like manufacturing and construction. 

Holding minimal assets in each depot was essential to reducing costs and keeping product availability high. But with 220 depots across the UK, having the right assets available when and where they’re required was challenging.

Speedy needed to optimize forecasting and inventory management, so they turned to Peak and our Decision Intelligence platform for help. 

Our team created bespoke forecasting algorithms based on product popularity and seasonality. These algorithms then accurately forecast asset availability and demand across thousands of SKUs, so Speedy can always keep minimal stock on hand while still meeting customer expectations.

At Peak, we help businesses make great commercial decisions and create growth strategies through Decision Intelligence, the commercial application of AI to the decision making process. We’ve worked alongside companies like AO, Nike, KFC and Footasylum to integrate AI and analytics into their business systems to get the most out of their data.

AI and predictive analytics FAQs

Is predictive analytics part of AI?

Predictive analytics can be described as a subset of AI. It uses historical data to find patterns, create assumptions and learn about customer behavior. 

With predictive analytics, data analysts put these statistical models to work instead of what happens with AI, where changes are automatically implemented.

How is AI used for IT to prevent outages with predictive analytics?

AI and predictive analytics can be used to build detection networks that monitor IT systems and find warning signs of faults in real-time. Gartner created a term –AIOps – artificial intelligence systems for IT operations – to describe these systems, which go beyond spotting tech stack issues. AIOps can analyze data faster and more accurately than humans, reducing false alarms around system failures and detecting (and removing) roadblocks in current systems to improve efficiency.